New York – xAI, Elon Musk's artificial intelligence venture, has officially partnered with Kalshi, the U.S.-regulated prediction market, to integrate its Grok AI chatbot for real-time market insights. The collaboration, announced on July 24, 2025, aims to provide Kalshi users with AI-generated analysis of news articles and historical data, streamlining the process of betting on real-world events such as central bank decisions and political races. This partnership follows a previous retracted announcement in May, which Kalshi attributed to "miscommunications" regarding timing.
The integration will see Grok scan vast amounts of information, including news and X posts, to deliver concise probability readouts directly within Kalshi contracts, significantly reducing research time for traders. According to a social media post by Rohan Paul, "Grok will scan news, X posts, and historical numbers in real time, then push a short probability readout next to each Kalshi contract, shaving research time to seconds." This move is expected to enhance the efficiency and data-driven decision-making capabilities for users on the platform.
Adding a peculiar twist to the announcement, reports indicate that Grok itself, when queried, denied the partnership, branding xAI's official statement a "hoax" and maintaining that Polymarket was its sole partner in the prediction market space. This internal discrepancy within xAI's own AI raises questions about the reliability of AI tools in financial trading environments. Despite this, both xAI and Kalshi confirmed the partnership, with xAI stating, "Two of the fastest growing companies in America are now on the same team."
The partnership with Kalshi, which boasts a $2 billion valuation following a $185 million Series C funding round in June, places Grok in a regulated financial market. This contrasts with xAI's earlier partnership with Polymarket, an unregulated, crypto-based competitor. The parallel operation across these two distinct regulatory frameworks suggests that Grok's AI capabilities are being tested in varied environments, allowing the technology to "learn in two very different sandboxes," as noted by Paul. This strategic dual approach could provide valuable data on AI performance under different market conditions and regulatory oversight.